Modelling outlet power loss in Archimedes screw generators
Bibliographic record
Abstract
Abstract Archimedes screw generators are a small-scale, eco-friendly hydropower technology. Despite their promise as a sustainable energy technology, the design specifics of the technology are not well documented in the published literature. Existing performance prediction models often fail to accurately forecast power loss, particularly as it relates to the outlet of the screw generator. To address this, a comprehensive computational fluid dynamic model was developed and evaluated using both laboratory-scale experiments and real-world data. This yielded an extensive dataset that covered wide variations in design parameters. The dataset was then used to inform the development and evaluation of an outlet power loss prediction model. The resulting model significantly improved the accuracy of overall performance predictions, reducing average error to 13.68 % compared with nominal experimental data – a substantial improvement over previous models, which averaged around 42.55 % error for the same test cases. Notably, the new model achieved an absolute error of 5 % or less in over 26 % of comparison points, marking a remarkable advancement by predicting outlet power loss by more than 28.8 %.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".